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王桥.2021.中国环境遥感监测技术进展及若干前沿问题.遥感学报，25（1）： 25-36
Wang Q. 2021. Progress of environmental remote sensing monitoring technology in China and some related frontier issues. National Remote Sensing Bulletin， 25（1）：25-36
新时期中国环境污染防治和生态文明建设的迫切需求催生了环境遥感监测技术的迅速发展，20多年来，中国环境遥感监测从无到有，从弱到强，逐渐进入到国家生态环境保护的主战场，并成为国家环境管理决策不可或缺的重要技术手段，发挥了关键性支撑作用。面对实行最严格的生态环境保护制度、科学治污、精准治污、依法治污的新形势和新要求，中国环境遥感监测面临前所未有的机遇和挑战。本文首先以环境监测卫星建设和应用为主线，回顾了中国环境遥感监测工作历程；然后从典型关键技术研究概况入手，总结了水环境遥感监测、大气环境监测、生态环境监测关键技术的进展；最后，结合高性能对地观测、大数据等先进技术的发展，对后续环境卫星、生态环境问题遥感主动发现、环境遥感监测大数据、基于深度学习的环境遥感反演等前沿问题进行了探讨，指出了中国环境遥感急需解决的问题和发展方向，并对相关技术的发展和应用前景进行了分析和展望。可以看到，在国家有关部门的高度重视和作者所带领团队及大量相关研究人员的共同努力下，中国已初步建立了环境遥感监测技术体系，并形成了业务化应用能力，以高分辨率探测为核心的新一代环境监测卫星正在得到快速发展，环境专用卫星载荷的技术性能将得到大幅提升，同时，环境遥感机理研究正得到进一步加强，环境遥感监测的精度和效率将得到进一步提升，中国环境遥感监测正在与人工智能、大数据等新技术加速融合，从以数理建模为核心的模型驱动时代进入到以智能感知为特征的数据驱动时代，由此将催生新的环境遥感应用场景和大数据产品不断涌现，推动环境遥感监测向智能感知、智能预警、智能决策、智能服务的方向发展。
The urgent need of environmental pollution prevention and ecological civilization construction in the new period has promoted the rapid development of environmental remote sensing monitoring technology. Over the past 20 years and more
China’s environmental remote sensing monitoring has gradually entered the main battlefield of national ecological and environmental protection from scratch. It has become an indispensable and important technical means for national environmental management and decision making
playing a key supporting role. Faced with the new situation and new requirements of implementing the strictest ecological and environmental protection system
scientific
precise
and law-based pollution control
China’s environmental remote sensing monitoring is faced with unprecedented opportunities and challenges. This paper first reviews the work history of environmental remote sensing monitoring in China with the construction and application of environmental monitoring satellites as the main line. Starting with the research overview of typical key technologies
this paper then summarizes the progress of key technologies of water environment remote sensing monitoring
atmospheric environment monitoring
and ecological environment monitoring. Combined with the development of advanced technologies
such as high-performance Earth observation and big data
the frontier issues of subsequent environmental satellites
active remote sensing discovery of ecological environmental problems
big data of remote sensing monitoring
and environmental remote sensing inversion based on deep learning
are discussed. The problems and development direction of environmental remote sensing in China are pointed out. The development and application prospects of relevant technologies are analyzed and prospected. The national environmental remote sensing monitoring technology system has been successfully established
and the operational capability of environmental remote sensing has been basically formed. These achievements are due to the great attention of relevant national departments and the joint efforts of the author’s team and relevant researchers. A new generation of environmental monitoring satellites featuring high-resolution detection is rapidly developing
The technical performance of the environmental satellite payloads will be greatly improved. The study of environmental remote sensing mechanism is attracting much attention
and the accuracy and efficiency of environmental remote sensing monitoring will be improved. China’s environmental remote sensing monitoring is integrated with artificial intelligence
big data
and other new technologies
and accelerates from the model-driven era centered on mathematical modeling to the data-driven era characterized by intelligent perception. These conditions will give birth to new environmental remote sensing application scenarios and big data products and make environmental remote sensing monitoring develop toward intelligent perception
intelligent warning
intelligent decision
and intelligent service.
生态环境遥感监测高性能环境卫星大数据深度学习
remote sensing monitoring of ecological environmenthigh performance environment satellitebig data of environmental monitoringdeep learning
Wolanin A, Camps-Valls G, Gómez-Chova L, Mateo-García G, van der Tol C, Zhang YG and Guanter L. 2019. Estimating crop primary productivity with Sentinel-2 and Landsat 8 using machine learning methods trained with radiative transfer simulations. Remote Sensing of Environment, 225: 441-457 [DOI: 10.1016/j.rse.2019.03.002http://dx.doi.org/10.1016/j.rse.2019.03.002]
Ai B, Wen Z, Jiang Y C, Gao S and Lv G N. 2019. Sea surface temperature inversion model for infrared remote sensing images based on deep neural network. Infrared Physics and Technology, 99: 231-239 [DOI: 10.1016/j.infrared.2019.04.022http://dx.doi.org/10.1016/j.infrared.2019.04.022]
Zhang C, Sargent I, Pan X, Li H P, Gardiner A, Hare J and Atkinson P M. 2019. Joint Deep Learning for land cover and land use classification. Remote Sensing of Environment, 221: 173-187 [DOI: 10.1016/j.rse.2018.11.014http://dx.doi.org/10.1016/j.rse.2018.11.014]
Chen B Q, Wu Z X, Wang J K, Dong J W, Guan L M, Chen J M, Yang K and Xie G S. 2015. Spatio-temporal prediction of leaf area index of rubber plantation using HJ-1A/1B CCD images and recurrent neural network. ISPRS Journal of Photogrammetry and Remote Sensing, 102: 148-160 [DOI: 10.1016/j.isprsjprs.2014.12.011http://dx.doi.org/10.1016/j.isprsjprs.2014.12.011]
Chen K Y, Chen H X, Zhou C L, Huang Y C, Qi X Y, Shen R Q, Liu F R, Zuo M, Zou X Y, Wang J F, Zhang Y, Chen X G, Deng Y F and Ren H Q. 2020. Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data. Water Research, 171: 115454 [DOI: 10.1016/j.watres.2019.115454http://dx.doi.org/10.1016/j.watres.2019.115454]
Gao Y N, Li Q, Wang S S and Gao J F. 2018. Adaptive neural network based on segmented particle swarm optimization for remote-sensing estimations of vegetation biomass. Remote Sensing of Environment, 211: 248-260 [DOI: 10.1016/j.rse.2018.04.026http://dx.doi.org/10.1016/j.rse.2018.04.026]
Yim I, Shin J, Lee H, Park S, Nam G, Kang T, Cho K H and Cha Y. 2020. Deep learning-based retrieval of cyanobacteria pigment in inland water for in-situ and airborne hyperspectral data. Ecological Indicators, 110: 105879 [DOI: 10.1016/j.ecolind.2019.105879http://dx.doi.org/10.1016/j.ecolind.2019.105879]
Jin X L, Li Z H, Feng H K, Ren Z B and Li S K. 2020. Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index. The Crop Journal, 8(1): 87-97 [DOI: 10.1016/j.cj.2019.06.005http://dx.doi.org/10.1016/j.cj.2019.06.005]
Peterson K T, Sagan V and Sloan J J. 2020. Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing. GIScience and Remote Sensing, 57(4): 510-525 [DOI: 10.1080/15481603.2020.1738061http://dx.doi.org/10.1080/15481603.2020.1738061]
Li D R, Tong Q X, Li R X, Gong J Y and Zhang L P. 2012. Current issues in high-resolution earth observation technology. Science China Earth Sciences, 55(7): 1043-1051
李德仁, 童庆禧, 李荣兴, 龚健雅, 张良培. 2012. 高分辨率对地观测的若干前沿科学问题. 中国科学: 地球科学, 42(6): 805-813 [DOI: 10.1007/s11430-012-4445-9http://dx.doi.org/10.1007/s11430-012-4445-9]
Li J, Pei Y Q, Zhao S H, Xiao R L, Sang X and Zhang C Y. 2020. A review of remote sensing for environmental monitoring in China. Remote Sensing, 12(7): 1130 [DOI: 10.3390/rs12071130http://dx.doi.org/10.3390/rs12071130]
Li T W, Shen H F, Yuan Q Q, Zhang X C and Zhang L P. 2017. Estimating ground-level PM2.5 by fusing satellite and station observations: a geo-intelligent deep learning approach. Geophysical Research Letters, 44(23): 11985-11993 [DOI: 10.1002/2017GL075710http://dx.doi.org/10.1002/2017GL075710]
Liu J G, Meng D S, Gui H Q, Yin G F, Zhang F, Zhang S, Zhang H Z and Wang J N. 2018. Development and prospects of disruptive technologies in environmental monitoring. Chinese Engineering Science, 20(6): 50-56
刘建国, 孟德硕, 桂华侨, 殷高方, 张甫, 张曙, 张红振, 王金南. 2018. 环境监测领域颠覆性技术的发展与展望. 中国工程科学, 20(6): 50-56
Joharestani M Z, Cao C X, Ni X L, Bashir B and Talebiesfandarani S. 2019. PM2.5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data. Atmosphere, 10(7): 373 [DOI: 10.3390/atmos10070373http://dx.doi.org/10.3390/atmos10070373]
Pyo J C, Duan H T, Baek S, Kim M S, Jeon T, Kwon Y S, Lee H and Cho K H. 2019. A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery. Remote Sensing of Environment, 233: 111350 [DOI: 10.1016/j.rse.2019.111350http://dx.doi.org/10.1016/j.rse.2019.111350]
Qin K, Han X, Li D H, Xu J, Loyola D, Xue Y, Zhou X R, Li D, Zhang K F and Yuan L M. 2020. Satellite-based estimation of surface NO2 concentrations over east-central China: a comparison of POMINO and OMNO2d data. Atmospheric Environment, 224: 117322 [DOI: 10.1016/j.atmosenv.2020.117322http://dx.doi.org/10.1016/j.atmosenv.2020.117322]
Tan J C, NourEldeen N, Mao K B, Shi J C, Li Z L, Xu T R and Yuan Z J. 2019. Deep learning convolutional neural network for the retrieval of land surface temperature from AMSR2 data in China. Sensors, 19(13): 2987 [DOI: 10.3390/s19132987http://dx.doi.org/10.3390/s19132987]
Kleynhans T, Montanaro M, Gerace A and Kanan C. 2017. Predicting top-of-atmosphere thermal radiance using MERRA-2 atmospheric data with deep learning. Remote Sensing, 9(11): 1133 [DOI: 10.3390/rs9111133http://dx.doi.org/10.3390/rs9111133]
Vasit S, Kyle T, Peterson, et al) Sagan V, Peterson K T, Maimaitijiang M, Sidike P, Sloan J, Greeling B A, Maalouf S and Adams C. 2020. Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing. Earth-Science Reviews, 205: 103187 [DOI: 10.1016/j.earscirev.2020.103187http://dx.doi.org/10.1016/j.earscirev.2020.103187]
Wang Q, Li Q, Chen L F, Zhang M G and Zhang X Y. 2011. Atmospheric Environment Satellite Remote Sensing Technology and its Application. Beijing: Science Press
王桥, 厉青, 陈良富, 张美根, 张兴赢. 2011. 大气环境卫星遥感技术及其应用. 北京: 科学出版社
Wang Q, Li Q, Wang Z T, Chen H, Mao H Q and Chen C H. 2017. A quantitatively operational judging method for the process of large regional heavy haze event based on satellite remote sensing and numerical simulations. Atmosphere, 8(11): 222 [DOI: 10.3390/atmos8110222http://dx.doi.org/10.3390/atmos8110222]
Wang Q, Li Q, Gao J, Wang Z F and Yu H Y. 2017. PM2.5 Monitoring Technology and Application using Satellite Remote Sensing. Beijing: China Environmental Press
王桥, 厉青, 高健, 王自发, 于海燕. 2017. PM2.5卫星遥感技术及其应用. 北京: 中国环境出版社
Wang Q and Liu S H. 2016. Research and implementation of national environmental remote sensing monitoring system. Journal of Remote Sensing, 20(5): 1161-1169
王桥, 刘思含. 2016. 国家环境遥感监测体系研究与实现. 遥感学报, 20(5): 1161-1169 [DOI: 10.11834/jrs.20166201http://dx.doi.org/10.11834/jrs.20166201]
Ministry of Environmental Protection. 2014. Remote sensing survey and assessment of ten years' change of ecological environment in China (2000-2010). Beijing: Science Press
环境保护部. 2014. 全国生态环境十年变化(2000—2010年)遥感调查与评估. 北京: 科学出版社
Wang Q, Sun D Y, Li Y M, Le C F and Huang C C. 2010. Mechanisms of remote-sensing reflectance variability and its relation to bio-optical processes in a highly turbid eutrophic lake: lake Taihu (China). IEEE Transactions on Geoscience and Remote Sensing, 48(1): 575-584 [DOI: 10.1109/TGRS.2009.2027316http://dx.doi.org/10.1109/TGRS.2009.2027316]
Wang Q, Wei B, Wang C Z and Zhang F. 2010. Remote Sensing Monitoring of Ecological Environment based on HJ-1 Satellite. Beijing: Science Press
王桥, 魏斌, 王昌佐, 张峰. 2010. 基于环境一号卫星的生态环境遥感监测. 北京: 科学出版社
Wang Q and Wu C Q. 2013. Application Principle and Case of Water Environment Remote Sensing. Beijing: Science Press
王桥, 吴传庆. 2013. 水环境遥感应用原理与案例. 北京: 科学出版社
Wang Q. 2015. Remote Sensing Methods and Applications in Regional Ecological Supervision. Beijing: China Environmental Press
王桥. 2015. 区域生态保护的遥感监管方法与应用. 北京: 中国环境出版社
Wang Q and Zhu L. 2018. Urban Black and Odorous Water Monitoring Technique and Application using Remote Sensing. Beijing: China Environmental Press
王桥, 朱利. 2018. 城市黑臭水体遥感监测技术与应用示范. 北京: 中国环境出版社
Xu Y M, Ho H C, Wong M S, Deng C B, Shi Y, Chan T C and Knudby A. 2018. Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM2.5. Environmental Pollution, 242: 1417-1426 [DOI: 10.1016/j.envpol.2018.08.029http://dx.doi.org/10.1016/j.envpol.2018.08.029]
Zhang B. 2018. Remotely sensed big data era and intelligent information extraction. Geomatics and Information Science of Wuhan University, 43(12): 1861-1871
张兵. 2018. 遥感大数据时代与智能信息提取. 武汉大学学报·信息科学版, 43(12): 1861-1871 [DOI: 10.13203/j.whugis20180172http://dx.doi.org/10.13203/j.whugis20180172]
Zhang Y S, Wu L, Ren H Z, Deng L C and Zhang P C. 2020. Retrieval of water quality parameters from hyperspectral images using hybrid Bayesian probabilistic neural network. Remote Sensing, 12(10): 1567 [DOI: 10.3390/rs12101567http://dx.doi.org/10.3390/rs12101567]
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